Multi-scale representation of an out of focus image

ABSTRACT

A method for generating a multi scale representation of an input image, the method comprising the procedures of: estimating a scale factor corresponding to said input image; determining a set of Gaussian difference kernels according to said estimated scale factor, and according to a predetermined set of Gaussian kernels; and generating a multi-scale representation of said input image by applying each of said set of Gaussian difference kernels on said input image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is the national phase of International (PCT) PatentApplication Serial No. PCT/IL2009/000055, filed on Jan. 14, 2009,published under PCT Article 21(2) in English, which claims priority toand the benefit of U.S. Provisional Patent Application No. 61/021,705,filed on Jan. 17, 2008, the disclosure of each of which is incorporatedherein by reference in its entirety.

FIELD OF THE DISCLOSED TECHNIQUE

The disclosed technique relates to multi-scale representation of asignal, in general, and to methods and systems for generating amulti-scale representation of an out-of-focus image, in particular.

BACKGROUND OF THE DISCLOSED TECHNIQUE

Multi-scale representation of an input image is employed in many visualprocessing applications such as feature detection (e.g. edge, blob,junction or ridge), feature classification, object recognition, objectclassification, image classification, shape analysis, and the like. Aplurality of images, each of the images is at a different scale, aregenerated by smoothing the input image with ascending Gaussian kernels.

Optical blurring of an input image, resulting from the input image beingout-of-focus, is modeled as a convolution of an input focused image witha Gaussian kernel of certain variance value. The value of the varianceof the convolving Gaussian kernel corresponds to the blur level of theinput image. Image convolution with a Gaussian kernel is described byequation (1):f*g(σ₃)=(f*g(σ₁))*g(σ₂); σ₃=√{square root over ((σ₁ ²+σ₂ ²))}  (1)

-   f—A focused image-   g—A Gaussian kernel of certain variance-   *—A convolution operator.    When the input image is blurred (i.e., out-of-focus input image),    generating a multi-scale representation of the input image, might    result in a removal of important details from the image (i.e., over    smoothing the input image). One way of overcoming the over-smoothing    problem of a blurred input image, is to reconstruct a focused image    from the blurred image by estimating the Gaussian kernel    corresponding to the blur level of the image, de-convolving the    input image with the estimated Gaussian kernel, and generating a    multi-scale representation of the de-convolved image.

Reference is now made to FIG. 1, which is a schematic illustration of amethod for generating a multi-scale representation of a blurred inputimage, operative as known in the art. In procedure 100, a Gaussiankernel, corresponding to the blur level of the input image, isestimated. The Gaussian kernel estimation is achieved by any of the blurlevel estimation techniques known in the art. In procedure 102, theblurred input image is de-convolved in order to reconstruct a focusedimage. The blurred image is de-convolved according to the Gaussiankernel, corresponding to the blur level of the image, estimated inprocedure 100. In procedure 104, a multi-scale representation of thede-convolved image is generated by convolving the de-convolved imagewith a plurality of Gaussian kernels of ascending values of variance. Inprocedure 106, a visual processing is performed on the multi-scalerepresentation of the input image.

Reference is now made to “Scale space theory in computer vision” by TonyLindeberg, a book published by Springer (1994). This publication isdirected at a formal framework, scale-space representation, for handlingthe notion of scale in image data. The book gives an introduction to thegeneral foundations of the scale space theory and shows how it appliesto essential problems in computer vision such as computation of imagefeatures.

Reference is now made to an article entitled “Estimating Image Blur inThe Wavelet Domain”, by Filip Rooms et al. This reference is directed toa method for estimating the blur level of an input image, according toinformation contained in the input image. A blurred image is modeled asthe corresponding focused image convolved with a Point Spread Function(PSF). The method includes the procedures of: calculating the Lipschitzexponent; generating a histogram; and estimating the blur of the imageaccording to the center of gravity of the histogram and according to themaximum of the histogram. The Lipschitz exponent is calculated in allpoints, where there is a change in intensity in either the horizontal orthe vertical direction. The histogram of the Lipschitz exponents, of theblurred image, is a single peak histogram with a certain distributionaround that peak. The blur level of the image is estimated according tothe center of gravity of the distribution around the peak and accordingto the maximum of the peak.

Reference is now made to an article entitled “Pyramid Method in ImageProcessing” written by E. H. Adelson et al. This reference is directedto a method for constructing an image pyramid of different resolutions.The image pyramid is employed for a variety of visual processingapplications such as pattern recognition. The image pyramid consists ofa sequence of copies of an original image in which both sample densityand resolution are decreased. The method includes the procedures ofconvolving the original image with a set of Gaussian-like weighingfunctions, subtracting each Gaussian pyramid level from the next lowerlevel in the pyramid, and interpolating sample values between those in agiven level before that level is subtracted from the next lower level.

A zero level of the pyramid is the original image. The convolutionprocedure acts as a low-pass filter with the band limit reduced by oneoctave, with each level, correspondingly. The procedures of subtractingand interpolating act as a band-pass filter. The procedure ofinterpolating is necessary since the subtraction is between levels ofdifferent sample densities.

SUMMARY OF THE PRESENT DISCLOSED TECHNIQUE

It is an object of the disclosed technique to provide a novel method andsystem for generating a multi-scale representation of an inputout-of-focus image, which overcomes the disadvantages of the prior art.

In accordance with the disclosed technique, there is thus provided amethod for generating a multi-scale representation of an input image.The method comprising the procedures of: estimating a scale factorcorresponding to the input image; determining a set of Gaussiandifference kernels; and generating a multi-scale representation of theinput image.

The procedure of determining the set of Gaussian difference kernels isperformed according to the estimated scale factor, and according to apredetermined set of Gaussian kernels. The procedure of generating amulti-scale representation is performed by applying each of the set ofGaussian difference kernels on the input image.

In accordance with another aspect of the disclosed technique, there isthus provided a system for generating a multi-scale representation of aninput image, the system comprising: a scale space level estimator; and amulti-scale representation generator. The scale space level estimatorestimates a scale factor corresponding to the input image. Themulti-scale representation generator is coupled with the scale spacelevel estimator.

The multi-scale representation generator receives the estimated scalefactor, and determines a set of Gaussian difference kernels according tothe estimated scale factor and according to a predetermined set ofGaussian kernels. The multi-scale representation generator furthergenerates a multi-scale representation of the input image by applyingeach of the set of Gaussian difference kernels to the input image.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed technique will be understood and appreciated more fullyfrom the following detailed description taken in conjunction with thedrawings in which:

FIG. 1, is a schematic illustration of a method for generating amulti-scale representation of a blurred input image, operative as knownin the art;

FIG. 2, is a schematic illustration of a scale space representation ofan input image, constructed in accordance with an embodiment of thedisclosed technique;

FIG. 3, is a schematic illustration of a system for generating amulti-scale representation of a blurred input image, generallyreferenced 160, constructed and operative in accordance with anotherembodiment of the disclosed technique;

FIG. 4, is a schematic illustration of a multi-scale representationgenerating method, operative in accordance with a further embodiment ofthe disclosed technique;

FIG. 5, is a schematic illustration of a method for generating amulti-scale representation (procedure 196 of FIG. 4), operative inaccordance with another embodiment of the disclosed technique;

FIG. 6A, is a schematic illustration of a multi-scale representation ofan input focused image, generally referenced 250, constructed inaccordance with a further embodiment of the disclosed technique; and

FIG. 6B, is a schematic illustration of a multi-scale representation ofan input blurred image, generally referenced 264, constructed inaccordance with another embodiment of the disclosed technique.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The disclosed technique overcomes the disadvantages of the prior art bygenerating a multi-scale representation of a hypothetic focused imagethat relates to the blurred input image, without reconstruction of thefocused image. The multi-scale representation starts at a scale level,which is lower than that of the blurred input image.

According to one embodiment of the disclosed technique, the multi-scalerepresentation of the hypothetic focused image is produced by theprocedures of estimating the blur level of a blurred input image (i.e.,estimating the value of the variance of the Gaussian kernelcorresponding to the blur level of the image—the scale factor),generating a set of Gaussian difference kernels from a set ofpre-determined Gaussian kernels, and convolving the input image with theset of Gaussian difference kernels.

The value of variance of each of the set of Gaussian difference kernelsis determined from formula (2):σ_(diff)=√{square root over (σ_(required) ²−σ_(blur) ²)}  (2)

-   σ_(diff)—is the value of variance of a specific Gaussian difference    kernel.-   σ_(required)—is the value of variance of a predetermined difference    Gaussian, corresponding to the specific Gaussian difference kernel.-   σ_(blur)—is the estimated value of variance of the blurred input    image (i.e., the blur level).    Formula (2) is derived from formula (1). It is noted, that when the    value of variance of the estimated Gaussian kernel is higher than    that of a certain Gaussian kernel, that certain Gaussian kernel is    omitted from the set of Gaussian difference kernels.

The term “Multi-scale representation” herein below refers to arepresentation of an image by a set of images, each of the imagesrelating to a different scale. Each of the images in the multi-scalerepresentation is generated by convolving the original image with arespective Gaussian kernel. The value of variance of a Gaussian kernel,employed for generating an image of the multi-scale representation, isreferred to as a scale factor. The scale factors of the multi-scalerepresentation are of ascending order. By convolving the input imagewith a Gaussian kernel, image structures of spatial size, whichcorresponds to the scale factor, are removed from that image (i.e.,image structures are smoothed from the image by convolution). The term“Scale space level” herein below refers to the position of an imagewithin a multi-scale representation (i.e., a scale space levelcorresponds to the value of the variance of a Gaussian kernel—scalefactor).

Reference is now made to FIG. 2, which is a schematic illustration of ascale space representation of an input image, constructed in accordancewith an embodiment of the disclosed technique. FIG. 2 includes an inputimage 130, a first image 132, a second image 134, a third image 136, afourth image 138, and a fifth image 140. First image 132 is generated byapplying a first Gaussian (not shown) having a first scale factor (notshown) to input image 130. Second image 134 is generated by applying asecond Gaussian (not shown) having a second scale factor (not shown) toinput image 130. It is noted, that the second scale factor is largerthan the first scale factor.

Third image 136 is generated by applying a third Gaussian (not shown)having a third scale factor (not shown) to input image 130. It is noted,that the third scale factor is larger than the second scale factor.Fourth image 138 is generated by applying a fourth Gaussian (not shown)having a fourth scale factor (not shown) to input image 130. It isnoted, that the fourth scale factor is larger than the third scalefactor. Fifth image 140 is generated by applying a fifth Gaussian (notshown) having a fifth scale factor (not shown) to input image 130. It isnoted, that the fifth scale factor is larger than the fourth scalefactor.

Reference is now made to FIG. 3, which is a schematic illustration of asystem for generating a multi-scale representation of a blurred inputimage, generally referenced 160, constructed and operative in accordancewith another embodiment of the disclosed technique. System 160 includesan image source 162, a scale space level estimator 164, a multi-scalerepresentation generator 166, and a visual processor 168. Image source162 is coupled with scale space level estimator 164. Space levelestimator 164 is coupled with multi-scale representation generator 166.Multi-scale representation generator 166 is coupled with applicationprocessor 168. It is noted that, either any pair of, or all of scalespace level estimator 164, multi-scale representation generator 166, andvisual processor 168 can be integrated together on a single processor.

Image source 162 provides an input image (not shown) to scale spacelevel estimator 164. Image source 162 can be an image capture device, astorage unit storing the input image, a communication interfacereceiving the input image from an external source (e.g., a network), andthe like. Scale space level estimator 164 estimates the scale factorcorresponding to the input image (i.e., the scale factor of the Gaussiankernel employed for modeling the input image). The scale factorestimation can be achieved by any of the methods known in the art. Scalespace level estimator 164 sends the input image and the estimated scalefactor of the input image to multi-scale representation generator 166.

Multi-scale representation generator 166 determines the scale spacelevel of the input image, according to the estimated scale factorthereof. Multi-scale representation generator 166 predetermines a set ofGaussian kernels, each of the Gaussian kernels having scale factorhigher than that of the previous Gaussian kernel in the set.

Multi-scale representation generator 166 substitutes each of the valuesof variance of the pre-determined Gaussian kernels with σ_(required) offormula (2):σ_(diff)=√{square root over (σ_(required) ²−σ_(blur) ²)})for generating each of a set of Gaussian difference kernels,respectively. Multi-scale representation generator 166 omits from theset of Gaussian difference kernels every Gaussian kernel having negativescale factor. A negative scale factor is received for each of the set ofpredetermined Gaussian kernels having scale factor lower than that ofthe estimated scale factor corresponding to the input image.

Multi-scale representation generator 166 applies each of the Gaussiandifference kernels to the input image, for generating a scaled image ofthe input image. The set of the generated scaled images is referred toas a multi-scale representation of the input image. Multi-scalerepresentation generator sends the multi-scale representation of theinput image to visual processor 168. Visual processor 168 performs avisual processing, such as feature detection, feature classification,object recognition, object classification, image classification, shapeanalysis, and the like, on the multi-scale representation of the inputimage.

Reference is now made to FIG. 4, which is a schematic illustration of amulti-scale representation generating method, operative in accordancewith a further embodiment of the disclosed technique. In procedure 190,an input image is received. With reference to FIG. 3, communicationinterface 162 receives an input image. In procedure 192, the scalefactor corresponding to the input image is estimated (i.e., the blurlevel of the input image is estimated). With reference to FIG. 3, scalespace level estimator 164 estimates the scale factor corresponding tothe input image.

In procedure 194, a set of Gaussian difference kernels are determinedaccording to the estimated scale factor and according to a predeterminedset of Gaussian kernels. The set of Gaussian difference kernels aredetermined by formula (2), according to the estimated scale factorcorresponding to the input image, and according to each of the scalefactors of the predetermined set of Gaussian kernels. The predeterminedset of Gaussian kernels is predetermined such that, the scale factors ofthe Gaussian kernels are of ascending order. With reference to FIG. 3,multi-scale representation generator 166 determines a set of Gaussiandifference kernels according to the estimated scale factor and accordingto a predetermined set of Gaussian kernels.

In procedure 196, a multi-scale representation of the input image isgenerated by applying each of the set of Gaussian difference kernels tothe input image. With reference to FIG. 3, multi-scale representationgenerator 166 applies each of the set of Gaussian difference kernels tothe input image, for generating a multi-scale representation of theinput image. Procedure 194 is further detailed in FIG. 5. In procedure198, a visual processing is performed on the multi-scale representationof the input image. With reference to FIG. 3, visual processor 168performs a visual processing (e.g., feature detection, featureclassification, object recognition, object classification, imageclassification, and shape analysis) on the multi-scale representation ofthe input image.

Reference is now made to FIG. 5, which is a schematic illustration of amethod for generating a multi-scale representation (procedure 196 ofFIG. 4), operative in accordance with another embodiment of thedisclosed technique. In procedure 220, a first image of a set of imagesof the multi-scale representation is generated by applying a Gaussiankernel having scale factor of √{square root over (σ_(M) ²−σ_(B) ²)}(i.e., scale factor), to the input image. σ_(B) Is the scale factor ofthe Gaussian kernel corresponding to the input image (i.e., the Gaussiankernel corresponding to the blur level of the input image). σ_(M) Is thescale factor of the first Gaussian kernel of a set of predeterminedGaussian kernels, which value is greater than σ_(B). σ_(B) is estimatedby scale space level estimator 164 of FIG. 3. With reference to FIG. 3,multi-scale representation generator 166 generates the first image ofthe multi-scale representation of the input image by applying a firstGaussian difference kernel to the input image.

In procedure 222, a second image of the multi-scale representation ofthe input image is generated by applying a Gaussian kernel having scalefactor of √{square root over (σ_(M+1) ²−σ_(B) ²)} to the input image.With reference to FIG. 3, multi-scale representation generator 166generates the second image of the multi-scale representation of theinput image by applying a second Gaussian difference kernel to the inputimage.

It is noted that, each of the images of the multi-scale representationof the input image are created by applying each of the Gaussiandifference kernels having scale factor greater than σ_(B), to the inputimage, starting at √{square root over (σ_(M) ²−σ_(B) ²)} and finishingat √{square root over (σ_(N) ²−σ_(B) ²)}. In procedure 224, a last imagein the multi-scale representation is generated by applying a Gaussiankernel having scale factor of √{square root over (σ_(N) ²−σ_(B) ²)} tothe input image. It is noted that, the number of images N (i.e., theactual number of images in the multi-scale representation is N−(M+1)),as well as the scale factors σ₁, σ₂, σ₃ . . . σ_(N) are predetermined bya user.

Reference is now made to FIGS. 6A and 6B. FIG. 6A is a schematicillustration of a multi-scale representation of an input focused image,generally referenced 250, constructed in accordance with a furtherembodiment of the disclosed technique. FIG. 6B is a schematicillustration of a multi-scale representation of an input blurred image,generally referenced 264, constructed in accordance with anotherembodiment of the disclosed technique.

With reference to FIG. 6A, multi-scale representation 250 includes afocused input image 252, a first convolved image 254, a second convolvedimage 256, an M convolved image 258, an (M+1) convolved image 260, andan N convolved image 262. Focused original image 252 can be modeled asan image convolved with a Gaussian kernel having scale factor of σ₀=0.Multi-scale representation generator 166 (FIG. 3) generates firstconvolved image 254 by convolving original image 252 with a Gaussiankernel having scale factor of σ₁.

Multi-scale representation generator 166 generates second convolvedimage 256 by convolving original image 252 with a Gaussian kernel havingscale factor of σ₂. The value of σ₂ is greater than that of σ₁.Multi-scale representation generator 256 generates M convolved image 258by convolving original image 252 with a Gaussian kernel having scalefactor of σ_(M). The value of σ_(M) is greater than that of σ_(M+1)(i.e., the scale factor of the previous Gaussian kernel, of the set ofpredetermined Gaussian kernels, —not shown). Multi-scale representationgenerator 166 generates (M+1) convolved image 260 by convolving originalimage 252 with a Gaussian kernel having scale factor of σ_(M+1). Thevalue of σ_(M+1) is greater than that of σ_(M).

Multi-scale representation generator 166 generates all the images ofmulti-scale representation 250 in a manner similar to that describedherein above with reference to FIG. 6A. The last image of multi-scalerepresentation 250 is N convolved image 262. Multi-scale representationgenerator 166 generates N convolved image 262 by convolving originalimage 252 with a Gaussian kernel having scale factor of σ_(N). The valueof σ_(N) is greater than that of any of the previous variances.

With reference to FIG. 6B, multi-scale representation 264 includes ablurred input image 266, an M convolved image 268, an M+1 convolvedimage 270, and an N convolved image 272. Blurred original image 266 canbe modeled as an image convolved with a Gaussian kernel having scalefactor of σ_(B). Multi-scale representation generator 166 (FIG. 3)generates a first convolved image (not shown) by convolving originalimage 266 with a Gaussian kernel having scale factor of σ₁−σ_(B). Incase the value of σ_(B) is greater than that of σ₁, Multi-scalerepresentation generator 166 does not generate the first convolved imageand starts the multi-scale representation of original image 266 from asecond convolved image.

Multi-scale representation generator 166 (FIG. 3) generates the secondconvolved image by convolving original image 266 with a Gaussian kernelhaving scale factor of σ₂−σ_(B). In case the value of σ_(B) is greaterthan that of σ₂, Multi-scale representation generator 166 does notgenerate the second convolved image and starts the multi-scalerepresentation of original image 266 from a third convolved image.

In the example set forth in FIG. 6B, the first Gaussian kernel of thepredetermined set of Gaussian kernels having scale factor higher thanσ_(B), is Gaussian kernel M. Multi-scale representation generator 166(FIG. 3) generates M convolved image 268 by convolving original image266 with a Gaussian kernel having scale factor of σ_(M)−σ_(B). It isnoted that, M convolved image 258 of FIGS. 6A and M convolved image 268of FIG. 6B are substantially similar, since convolution with a Gaussiankernel obeys equation (1) as described herein above.

Multi-scale representation generator 166 generates all the images ofmulti-scale representation 264 in a manner similar to that describedherein above with reference to FIG. 6B. The last image of multi-scalerepresentation 264 is the N convolved image 272. Multi-scalerepresentation generator 166 generates N convolved image 272 byconvolving original image 264 with a Gaussian kernel having scale factorof σ_(N)−σ_(B).

It is noted that, image M, M+1, . . . N of multi-scale representation250 (FIG. 6A) and images of M, M+1, . . . N of multi-scalerepresentation 264 (FIG. 6B) are substantially similar, respectively. Itis further noted that, the number of images in multi-scalerepresentation 264 is (N−(M+1)). It will be appreciated by personsskilled in the art that the disclosed technique is not limited to whathas been particularly shown and described hereinabove. Rather the scopeof the disclosed technique is defined only by the claims, which follow.

The invention claimed is:
 1. A method for generating a multi-scalerepresentation of a blurred input image, the blurred input imagecorresponding to an initial blur Gaussian kernel σ_(blur) , the methodcomprising the steps of: estimating, by a scale space level estimator,said initial blur Gaussian kernel σ_(blur) corresponding to said blurredinput image; determining, by a multi-scale representation generator, aset of Gaussian difference kernels according to said initial blurGaussian kernel σ_(blur), and according to a predetermined set ofGaussian kernels, each of said set of Gaussian difference kernels beingdetermined according to formula σ_(diff)=√{square root over(σ_(required) ²−σ_(blur) ²)} , wherein σ_(diff) is a value of varianceof a specific Gaussian difference kernel, and σ_(required) is a value ofvariance of a selected one of said set of predetermined Gaussiankernels, corresponding to said specific Gaussian difference kernel; andgenerating, by the multi-scale representation generator, a multi-scalerepresentation of said input image by applying each of said set ofGaussian difference kernels on said input image.
 2. The method accordingto claim 1, further comprising the procedure of receiving said inputimage before said procedure of estimating.
 3. The method according toclaim 1, wherein said method further comprising a procedure ofperforming at least one of feature detection and feature classification,on said multi-scale representation of said input image, after saidprocedure of generating.
 4. The method according to claim 1, whereinsaid method further comprising a procedure of performing at least one ofobject recognition and object classification, on said multi-scalerepresentationof said input image, after said procedure of generating.5. The method according to claim 1, wherein said method furthercomprising a procedure of performing at least one of imageclassification and shape analysis, on said multi-scale representation ofsaid input image, after said procedure of generating.
 6. The methodaccording to claim 1, wherein said procedure of determining a set ofGaussian difference kernels includes a sub procedure of determining saidpredeteremined set of Gaussian kernels, and wherein each of the Gaussiankernels of said predetermined set of Gaussian kernels, has scale factorhigher than that of a previous Gaussian kernel of said predetermined setof Gaussian kernels.
 7. The method according to claim 1, wherein saidprocedure of generating said multi-scale representation is performed byapplying each Gaussian difference kernel of said set of Gaussiandifference kernels to said input image.
 8. The method according to claim1, wherein said input image is a blurred version of a hypothetic focusedimage, said input image being substantially similar to an image receivedby applying a Gaussian kernel, having said estimated scale factor, onsaid hypothetic focused image.
 9. A system for generating a multi-scalerepresentation of a blurred input image, the blurred input imagecorresponding to an initial blur Gaussian kernel σ_(blur), the systemcomprising: a scale space level estimator for estimating said initialblur Gaussian kernel σ_(blur) corresponding to said blurred input image;a multi-scale representation generator coupled with said scale spacelevel estimator, for receiving said estimated scale factor, determininga set of Gaussian difference kernels according to said initial blurGaussian kernel σ_(blur) and according to a predetermined set ofGaussian kernels, and for generating a multi-scale representation ofsaid input image by applying each of said set of Gaussian differencekernels to said input image, wherein said multi-scale representationgenerator determining of said set of Gaussian difference kernelsaccording to the following formula: σ_(diff)=√{square root over(σ_(required) ²−σ_(blur) ²)} ,wherein σ_(diff) is the value of varianceof a specific Gaussisn difference kernel, and σ_(required) is the valueof variance of a selected one of said set of predetermined Gaussiankernels.
 10. The system according to claim 9, wherein said systemfurther comprises an image source coupled with said scale space levelestimator, for providing said input image to said scale space levelestimator.
 11. The system according to claim 10, wherein said imagesource is selected from the list consisting of: an image capture device;a storage unit storing the input image; and a communication interfacereceiving the input image from an external source.
 12. The systemaccording to claim 9, wherein said system further comprises a visualprocessor coupled with said multi-scale representation generator, saidvisual processor performing at least one of feature detection andfeature classification, on said multi-scale representation of said inputimage.
 13. The system according to claim 9, wherein said system furthercomprises a visual processor coupled with said multi-scalerepresentation generator, said visual processor performing at least oneof object recognition and object classification, on said multi-scalerepresentation of said input image.
 14. The system according to claim 9,wherein said system further comprises a visual processor coupled withsaid multi-scale representation generator, said visual processorperforming at least one of image classification and shape analysis, onsaid multi-scale representation of said input image.
 15. The systemaccording to claim 9, wherein said multi-scale representation generatordetermines said predetermined set of Gaussian kernels, and wherein eachof said predetermined set of Gaussian kernels has scale factor higherthan that of a previous Gaussian kernel of said predetermined set ofGaussian kernels.
 16. The system according to claim 9, wherein saidmulti-scale representation generator generates said multi-scalerepresentation by applying each Gaussian difference kernel of said setof Gaussian difference kernels to said input image.
 17. The systemaccording to claim 9, wherein said input image is a blurred version of ahypothetic focused image, said input image being substantially similarto an image received by applying a Gaussian kernel, having saidestimated scale factor, on said hypothetic focused image.